AI-Driven Network Slicing for 5G UAV Connectivity Using SDN and Graph Attention Networks in NS-3

AI-Driven Network Slicing for 5G UAV Connectivity Using SDN and Graph Attention Networks in NS-3 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI-Driven Network Slicing for 5G UAV Connectivity Using SDN and Graph Attention Networks in NS-3 Khalid Aljonubi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8585548/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The integration of Unmanned Aerial Vehicles (UAVs) into 5G networks presents a major challenge in ensuring ultra-reliable, low-latency communication (URLLC) under dynamic conditions and diverse user demands. Network slicing, enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV), offers a promising solution by creating isolated virtual networks tailored to service requirements. However, static slicing mechanisms struggle to adapt to rapid traffic fluctuations and UAV mobility, resulting in inefficient resource use and degraded Quality of Service (QoS). This paper proposes an AI-driven dynamic network slicing framework to optimize UAV connectivity in a simulated 5G New Radio (NR) environment. To intelligently predict network states and allocate resources, we implement and compare two deep learning models: a Graph Attention Network (GAT) and a Long Short-Term Memory (LSTM) network. GAT has been chosen for its capacity to capture spatio-temporal dependencies in the network graph through attention mechanisms, which illustrate interactions among users, base stations, and UAVs. The framework was validated by conducting extensive simulations in NS-3 using the 5G-LENA module. A multi-slice environment is modeled with diverse traffic types and realistic mobility patterns, including Gauss-Markov for UAVs and Random Waypoint for ground users, across three congestion scenarios. The performance was evaluated based on throughput, latency, packet loss, and resource utilization. Results show that the GAT-based framework consistently outperforms the LSTM baseline, achieving superior congestion prediction, reduced latency, and improved resource allocation. These findings highlight the role of AI-enhanced slicing in meeting UAV application demands and provide a foundation for intelligent resource management in next-generation wireless networks. 5G 6G AI-driven network slicing dynamic resource allocation NS-3 simulation Software-Defined Networking (SDN) Graph Attention Networks (GAT) UAV communication Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor revision 03 Mar, 2026 Reviewers agreed at journal 17 Jan, 2026 Reviewers invited by journal 16 Jan, 2026 Editor assigned by journal 14 Jan, 2026 First submitted to journal 14 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8585548","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575667861,"identity":"627c1800-5b04-482f-b9ba-0c219c549468","order_by":0,"name":"Khalid Aljonubi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYLACHgYbOXn2BiDLwIJoLWnGhj0HQFokiNZyKLHhRgKISYQWg/OHj0m8bTtgzDjz+dUNPwokGPjbuxPwazlwLE1ybtsdOXbpnLKbPUCHSZw5uwG/loM9Zrd5254ZM87OSbvBA9RiIJFLQMth/m9ALYcTG26eSbv5hygtx3jYIFpusB+7TZQtkmfYzH/OOQcK5By22zIGEjwE/cJ3/vBjgzdloKg8/uzmmz82cvztvfi1KByAM3kMwCRe5SAg3wBnsj8gqHoUjIJRMApGJgAA/mdNScL4c+YAAAAASUVORK5CYII=","orcid":"","institution":"King Abdulaziz City for Science And Technology","correspondingAuthor":true,"prefix":"","firstName":"Khalid","middleName":"","lastName":"Aljonubi","suffix":""}],"badges":[],"createdAt":"2026-01-12 22:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8585548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8585548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100639714,"identity":"b37ad729-f488-4f41-bca4-06bd78ef0f93","added_by":"auto","created_at":"2026-01-20 02:50:30","extension":"bib","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10713,"visible":true,"origin":"","legend":"","description":"","filename":"snbibliography.bib","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/d885a58711b46a495bf55fd2.bib"},{"id":100796170,"identity":"6ac2af8b-dd2c-4fee-b551-b9f11d6d4c0d","added_by":"auto","created_at":"2026-01-21 13:41:14","extension":"cls","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55857,"visible":true,"origin":"","legend":"","description":"","filename":"snjnl.cls","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/aeb236804817cde5cf5df97c.cls"},{"id":100796376,"identity":"ee166d11-3766-43a4-8971-200d35424f31","added_by":"auto","created_at":"2026-01-21 13:42:52","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6668,"visible":true,"origin":"","legend":"","description":"","filename":"jwcnJWCND2600027.xml","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/6b2f93e1d26bab1596d93073.xml"},{"id":100796265,"identity":"845a0ac9-9480-436c-977e-ea6096224a9d","added_by":"auto","created_at":"2026-01-21 13:42:21","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1282,"visible":true,"origin":"","legend":"","description":"","filename":"JWCND260002714135.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/9b00bc7178f877699217fd94.xml"},{"id":102294783,"identity":"71211a18-8022-4d61-992d-99c70dc70166","added_by":"auto","created_at":"2026-02-10 09:56:41","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":910,"visible":true,"origin":"","legend":"","description":"","filename":"JWCND2600027Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/a1cee03ff780dfcf96a2d8a1.xml"},{"id":100639719,"identity":"d7785f28-c68c-4c8b-916a-b68442e6b407","added_by":"auto","created_at":"2026-01-20 02:50:30","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90880,"visible":true,"origin":"","legend":"","description":"","filename":"JWCND26000270enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/942ca3c812868fc8d3a1e9d2.xml"},{"id":100949318,"identity":"6a49c520-fa13-4a24-bf3b-67f69e6586d8","added_by":"auto","created_at":"2026-01-23 07:00:23","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39024,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/8202111bd65d735e6613528b.pdf"},{"id":100639721,"identity":"debe4005-57bd-499c-8ae2-2be53d6c0710","added_by":"auto","created_at":"2026-01-20 02:50:30","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39470,"visible":true,"origin":"","legend":"","description":"","filename":"figure102.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/9d79009f894a21a48d646e7d.pdf"},{"id":100639720,"identity":"b6bcc2e6-d56f-41d6-89c7-d9deddc4dd77","added_by":"auto","created_at":"2026-01-20 02:50:30","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31229,"visible":true,"origin":"","legend":"","description":"","filename":"figure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/baa5120f447b0a91c565408d.pdf"},{"id":100639725,"identity":"5301521f-f016-4509-871b-52a2cc22c262","added_by":"auto","created_at":"2026-01-20 02:50:30","extension":"pdf","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38412,"visible":true,"origin":"","legend":"","description":"","filename":"figure81.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/20fd5c3d110b930138b7dedb.pdf"},{"id":100639722,"identity":"7f7f2a24-3933-45a2-89d1-a2cbb62ec6d6","added_by":"auto","created_at":"2026-01-20 02:50:30","extension":"pdf","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40840,"visible":true,"origin":"","legend":"","description":"","filename":"figure92.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/bfc4286d8ad5c9f93776c367.pdf"},{"id":100639727,"identity":"e0d3f2c6-71e0-4243-a00f-594d197fde51","added_by":"auto","created_at":"2026-01-20 02:50:30","extension":"pdf","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37981,"visible":true,"origin":"","legend":"","description":"","filename":"sdn.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/dc37ba2130e45b0264910dde.pdf"},{"id":100796171,"identity":"64e0d331-eb89-47c8-8f4a-1144a44b0035","added_by":"auto","created_at":"2026-01-21 13:41:14","extension":"cls","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55857,"visible":true,"origin":"","legend":"","description":"","filename":"snjnl.cls","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/699f96ac3332d4815ef326a7.cls"},{"id":100804013,"identity":"a2251da4-7a7e-4cf8-9e82-791d726e4288","added_by":"auto","created_at":"2026-01-21 14:34:39","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99073,"visible":true,"origin":"","legend":"","description":"","filename":"JWCND26000270structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/5cc285c404c84bec790d44fd.xml"},{"id":100796177,"identity":"5c8c26a8-1629-4786-832b-98f5fd692a6c","added_by":"auto","created_at":"2026-01-21 13:41:22","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106578,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1/e8c2efe88da989723007ddea.html"},{"id":102299677,"identity":"39a134e9-f196-43b1-ab36-17a53e82f511","added_by":"auto","created_at":"2026-02-10 11:07:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":394564,"visible":true,"origin":"","legend":"","description":"","filename":"AIDrivenNetworkSlicingfor5GUAVConnectivityupdated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8585548/v1_covered_ab0f020b-2338-4e8b-8e90-bb3906a44b18.pdf"}],"financialInterests":"","formattedTitle":"AI-Driven Network Slicing for 5G UAV Connectivity Using SDN and Graph Attention Networks in NS-3","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"eurasip-journal-on-wireless-communications-and-networking","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jwcn","sideBox":"Learn more about [EURASIP Journal on Wireless Communications and Networking](http://jwcn-eurasipjournals.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jwcn/default.aspx","title":"EURASIP Journal on Wireless Communications and Networking","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"5G, 6G, AI-driven network slicing, dynamic resource allocation, NS-3 simulation, Software-Defined Networking (SDN), Graph Attention Networks (GAT), UAV communication","lastPublishedDoi":"10.21203/rs.3.rs-8585548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8585548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of Unmanned Aerial Vehicles (UAVs) into 5G networks presents a major challenge in ensuring ultra-reliable, low-latency communication (URLLC) under dynamic conditions and diverse user demands. Network slicing, enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV), offers a promising solution by creating isolated virtual networks tailored to service requirements. However, static slicing mechanisms struggle to adapt to rapid traffic fluctuations and UAV mobility, resulting in inefficient resource use and degraded Quality of Service (QoS). This paper proposes an AI-driven dynamic network slicing framework to optimize UAV connectivity in a simulated 5G New Radio (NR) environment. To intelligently predict network states and allocate resources, we implement and compare two deep learning models: a Graph Attention Network (GAT) and a Long Short-Term Memory (LSTM) network. GAT has been chosen for its capacity to capture spatio-temporal dependencies in the network graph through attention mechanisms, which illustrate interactions among users, base stations, and UAVs. The framework was validated by conducting extensive simulations in NS-3 using the 5G-LENA module. A multi-slice environment is modeled with diverse traffic types and realistic mobility patterns, including Gauss-Markov for UAVs and Random Waypoint for ground users, across three congestion scenarios. The performance was evaluated based on throughput, latency, packet loss, and resource utilization. Results show that the GAT-based framework consistently outperforms the LSTM baseline, achieving superior congestion prediction, reduced latency, and improved resource allocation. These findings highlight the role of AI-enhanced slicing in meeting UAV application demands and provide a foundation for intelligent resource management in next-generation wireless networks.\u003c/p\u003e","manuscriptTitle":"AI-Driven Network Slicing for 5G UAV Connectivity Using SDN and Graph Attention Networks in NS-3","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 02:50:25","doi":"10.21203/rs.3.rs-8585548/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2026-03-04T04:44:02+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-01-17T20:01:21+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-16T14:13:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-15T02:16:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"EURASIP Journal on Wireless Communications and Networking","date":"2026-01-14T14:57:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"eurasip-journal-on-wireless-communications-and-networking","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jwcn","sideBox":"Learn more about [EURASIP Journal on Wireless Communications and Networking](http://jwcn-eurasipjournals.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jwcn/default.aspx","title":"EURASIP Journal on Wireless Communications and Networking","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6a386844-de34-475f-9d40-8c43f3a2736b","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T03:52:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 02:50:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8585548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8585548","identity":"rs-8585548","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}